Improving Self-Localisation of Mobile Robots Based on Asynchronous Monte-Carlo Localization Method

This paper presents a set of robust and efficient algorithms with O(N) cost for the following situations: 1) object detection with a laser ranger; 2) mobile robot pose estimation and 3) an improved Monte-Carlo localization (MCL) method using multi-rate techniques. Object detection is mainly based on a novel multiple line fitting method for wall detection with regular constrained angles. Columns are also detected based on conventional circle fitting. In addition, two mobile robot self-localization methods are based on detected walls and columns respectively. These methods perform an efficient estimation based on LS sense and they can provide global pose estimation under assumption of known data-association. Moreover, the standard MCL method has been extended by considering the asynchronous sampling of sensors and actuators. Experimental results show that significant improvements in pose estimation can be obtained, since the algorithm runs at the fastest possible sampling frequencies for each process. This improvement allows the same accuracy as the standard MCL with fewer particles and lower computational cost.